﻿// #include <fstream>
// #include <sstream>
// #include <iostream>
// #include <opencv2/dnn.hpp>
// #include <opencv2/imgproc.hpp>
// #include <opencv2/highgui.hpp>

// using namespace cv;
// using namespace dnn;
// using namespace std;

// struct Net_config
// {
//     float confThreshold; // Confidence threshold
//     float nmsThreshold;  // Non-maximum suppression threshold
//     string modelpath;
// };

// class YOLOV7
// {
// public:
//     YOLOV7(Net_config config);
//     void detect(Mat &frame);

// private:
//     int inpWidth;
//     int inpHeight;
//     vector<string> class_names;
//     int num_class;

//     float confThreshold;
//     float nmsThreshold;
//     Net net;
//     void drawPred(float conf, int left, int top, int right, int bottom, Mat &frame, int classid);
// };

// YOLOV7::YOLOV7(Net_config config)
// {
//     this->confThreshold = config.confThreshold;
//     this->nmsThreshold = config.nmsThreshold;

//     this->net = readNet(config.modelpath);
//     ifstream ifs("static/coco.names");
//     string line;
//     while (getline(ifs, line))
//         this->class_names.push_back(line);
//     this->num_class = class_names.size();

//     size_t pos = config.modelpath.find("_");
//     int len = config.modelpath.length() - 6 - pos;
//     string hxw = config.modelpath.substr(pos + 1, len);
//     pos = hxw.find("x");
//     string h = hxw.substr(0, pos);
//     len = hxw.length() - pos;
//     string w = hxw.substr(pos + 1, len);
//     this->inpHeight = stoi(h);
//     this->inpWidth = stoi(w);
// }

// void YOLOV7::drawPred(float conf, int left, int top, int right, int bottom, Mat &frame, int classid) // Draw the predicted bounding box
// {
//     // Draw a rectangle displaying the bounding box
//     rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 0, 255), 2);

//     // Get the label for the class name and its confidence
//     string label = format("%.2f", conf);
//     label = this->class_names[classid] + ":" + label;

//     // Display the label at the top of the bounding box
//     int baseLine;
//     Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
//     top = max(top, labelSize.height);
//     // rectangle(frame, Point(left, top - int(1.5 * labelSize.height)), Point(left + int(1.5 * labelSize.width), top + baseLine), Scalar(0, 255, 0), FILLED);
//     putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1);
// }

// void YOLOV7::detect(Mat &frame)
// {
//     Mat blob = blobFromImage(frame, 1 / 255.0, Size(this->inpWidth, this->inpHeight), Scalar(0, 0, 0), true, false);
//     this->net.setInput(blob);
//     vector<Mat> outs;
//     this->net.forward(outs, this->net.getUnconnectedOutLayersNames());

//     int num_proposal = outs[0].size[0];
//     int nout = outs[0].size[1];
//     if (outs[0].dims > 2)
//     {
//         num_proposal = outs[0].size[1];
//         nout = outs[0].size[2];
//         outs[0] = outs[0].reshape(0, num_proposal);
//     }
//     /////generate proposals
//     vector<float> confidences;
//     vector<Rect> boxes;
//     vector<int> classIds;
//     float ratioh = (float)frame.rows / this->inpHeight, ratiow = (float)frame.cols / this->inpWidth;
//     int n = 0, row_ind = 0; /// cx,cy,w,h,box_score,class_score
//     float *pdata = (float *)outs[0].data;
//     for (n = 0; n < num_proposal; n++) /// ÌØÕ÷Í¼³ß¶È
//     {
//         float box_score = pdata[4];
//         if (box_score > this->confThreshold)
//         {
//             Mat scores = outs[0].row(row_ind).colRange(5, nout);
//             Point classIdPoint;
//             double max_class_socre;
//             // Get the value and location of the maximum score
//             minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
//             max_class_socre *= box_score;
//             if (max_class_socre > this->confThreshold)
//             {
//                 const int class_idx = classIdPoint.x;
//                 float cx = pdata[0] * ratiow; /// cx
//                 float cy = pdata[1] * ratioh; /// cy
//                 float w = pdata[2] * ratiow;  /// w
//                 float h = pdata[3] * ratioh;  /// h

//                 int left = int(cx - 0.5 * w);
//                 int top = int(cy - 0.5 * h);

//                 confidences.push_back((float)max_class_socre);
//                 boxes.push_back(Rect(left, top, (int)(w), (int)(h)));
//                 classIds.push_back(class_idx);
//             }
//         }
//         row_ind++;
//         pdata += nout;
//     }

//     // Perform non maximum suppression to eliminate redundant overlapping boxes with
//     // lower confidences
//     vector<int> indices;
//     dnn::NMSBoxes(boxes, confidences, this->confThreshold, this->nmsThreshold, indices);
//     for (size_t i = 0; i < indices.size(); ++i)
//     {
//         int idx = indices[i];
//         Rect box = boxes[idx];
//         this->drawPred(confidences[idx], box.x, box.y,
//                        box.x + box.width, box.y + box.height, frame, classIds[idx]);
//     }
// }

// int main()
// {
//     Net_config YOLOV7_nets = {0.3, 0.5, "static/models/yolov7-tiny_640x640.onnx"}; ////choices=["models/yolov7_640x640.onnx", "models/yolov7-tiny_640x640.onnx", "models/yolov7_736x1280.onnx", "models/yolov7-tiny_384x640.onnx", "models/yolov7_480x640.onnx", "models/yolov7_384x640.onnx", "models/yolov7-tiny_256x480.onnx", "models/yolov7-tiny_256x320.onnx", "models/yolov7_256x320.onnx", "models/yolov7-tiny_256x640.onnx", "models/yolov7_256x640.onnx", "models/yolov7-tiny_480x640.onnx", "models/yolov7-tiny_736x1280.onnx", "models/yolov7_256x480.onnx"]
//     YOLOV7 net(YOLOV7_nets);
//     string imgpath = "static/images/dog.jpg";
//     Mat srcimg = imread(imgpath);
//     net.detect(srcimg);

//     static const string kWinName = "Deep learning object detection in OpenCV";
//     namedWindow(kWinName, WINDOW_NORMAL);
//     imshow(kWinName, srcimg);
//     waitKey(0);
//     destroyAllWindows();
//     return 0;
// }
